Shale Gas Development:
Integrated Approach
Hemant Kumar Dixit
Mumbai, India
18 January-2013
SAMPLE IMAGE
Introduction

Motivation: Use seismic data to improve economics in resource
shale plays
Higher margins with less drilling and perforations/fracturing stages
– Minimize environmental impact
–

Challenges:
Sweetspot identification
– Optimize well location
– Optimize completions
–
Drilling
Installations
Completion
Motivation of Unconventional Resources

23% US gas production is from unconventional reservoirs (2010)

Coal stores 6-7 times more gas than conventional reservoirs

4 trillion bbl of oil in Canada oil sands and Venezuela heavy oil

Environment – proppant, water, noise, contamination
Source: Halliburton 2011-03
Challenges in Shale Explortaion
A mixture of water, sand and chemical
agents is injected at high pressure in
the well
0 ft
2,000 ft
The challenge: prediction
and control of fracturing
What seismic brings:
4,000 ft
Sand keeps
fissures open
Natural gas flows
from fissures
into well
Fissure
Mixture of water,
sand and chemical
agents
 Seismic Reservoir Characterization
 Stress & Fracture modeling
 Real-time Microseismic
Well
6,000 ft
Fissure
s
8,000 ft
10,000 ft
The shale is
fractured by the
pressure induced
in the well
Based on graphic by Al Granberg
CGGV North American Experience
More than 40 projects and 18,000 km2
Utica
2009 –
2 projects
178 Sq km
+ 2D
Regional
Marcellus
Horn River
2009 2011
8 Projects
1155 sq
Montneykm
2008 - 2011
6 Projects
1405 sq km
Bakken
2009 3 Projects
457 sq km
Picenace /
Uinta
2006 - 2008
3 Projects
+440 sq km
Eagle Ford
Barnett
Haynesville
2010 1 Project
340 sq km
2007 –
8 Projects
+500 sq
km
2009 2011
2 Projects
5607 sq km
2007 2011
5 Projects
726 sq km
Woodford
2010 2011
13
Projects
6920 sq
km
CGGV in Shale Resource Exploration
 Integrated solutions for Unconventional Resources
 Full suite of tools and technologies
 From prediction to monitoring
 Calibration & correlation with well data
Feasibility study
& survey design
Data acquisition
Processing &
Imaging
Fracture / stress
characterization
& rock properties
Sweet spot
prediction with
well-calibrated
attributes
Calibration with well data – correlation with production data
6
Microseismic
fracture
monitoring
Tri-Parish Line Case
Study
Generating Geomechanical
Properties and Sweet Spot
Identification for optimum
driling
Shale Plays: Questions?
Gas Content
Shale Type
Ductile or Britle
TOC, Bulk Volume of Gas
Fracture
Fracture Type, Direction and Length
Validation
Shale Plays: Seismic Driven Answers?
Poisson’s Ratio
Bulk Volume Gas
Young’s Modulus
Reservoir
Quality
Brittleness
Randomly oriented fractures
Stress
Closure Pressure
17
Shale Plays: Seismic Workflow
Haynesville Shale: Bulk Volume Gas
Bulk Volume Gas = Total Porosity x (1–Water Saturation)
Stress Analysis Workflow
Hooke’s Law / Linear Slip Theory
H
V
V
h
h
Patent
Seismic AzAVO Terms
E – Young’ s Modulus
n – Poisson’s Ratio
ZN – Normal Compliance
H
Differential Horizontal Stress Ratio (DHSR)
DHSR


H - h
H
hmin
If Hmax ≈ hmin (DHSR ≈ 0)
 Tensile cracks any direction
 || rock weakness
 Fracture network
If Hmax >> hmin (DHSR > 3-5%)
 Fractures || Hmax
 Shear Fractures
 Tensile Fractures
 Connect to existing fracture network for
production
Patent
Hmax
hmin = Closure Stress
Pressure
Hmax
Differential Horizontal Stress Ratio
Cross-plot DHSR vs. Young’s Modulus
Ductile
Brittle
Static Young’s Modulus
Aligned Fractures will form (YELLOW)
Fracture Swarms will occur (GREEN)
Ductile (RED)
DHSR platelets overlaying Young’s Modulus
BRITTLE
DHSR
H - h
H
Plate orientation: direction of maximum horizontal stress
Map colour: derived Young’s modulus
Volumetric Interpretation
Aligned Fractures (YELLOW)
Fracture Swarms (GREEN)
Ductile (RED)
16
h
H- h
H
H- h
H
H
Differential Horizontal Stress Ratio
Probable Zones of Better Hydraulic Fractures
Static Young’s Modulus
of Hydraulic
Fractures
Probability: ZonesPercentage
of better hydraulic
fractures
(random pattern)
Bottom of HVL
High
Low
Multi-Attribute Analysis
Highlighting Potential Good
Production Areas
High
Low
Validation: Analysis of orientation of H
Orientation H across the
Haynesville Shale derived compared with
from seismic
WEST
Triaxial Measurements and
Orientation H from
oriented core samples from
different depths in the
Haynesville Shale
EAST
The direction of maximum
horizontal stress predicted from
the seismic observations
matched the corresponding
core stress measurements to
within 5%.
-25 o
Conclusions
 Fully Integrated workflow for shale plays –
acquisition to interpretation
 Flexible multi-attribute solution correlating seismic
observations to production figures, using
 Geomechanical rock properties
 Stress – HTI
 Applications for:
 Sweet spot identification
 Well location optimization
 Completions optimization
20
Conclusions
 Environment
 Water access
 Proppant access
 Leakage prevention
 Financial
 Well costs reduced
 Well performance enhanced
 Return On Investment

SEISMIC can help!
21
Thank You
Reference:
Gray et. al.
Estimation of Stress and Geomechanical Properties using 3D Seismic
Data, First Break, Volume 30,March 2012
22
Differential Horizontal Stress Ratio (DHSR)

If Hmax ≈ hmin (DHSR ≈ 0)
hmin
 Tensile cracks any direction
 || rock weakness
 Fracture network
H - h
H
Hmax
hmin = Closure Stress

If Hmax >> hmin (DHSR > 35%)
 Fractures || Hmax
 Shear Fractures
 Tensile Fractures
 Connect to existing fracture
network for production
Pressure
Hmax
DHSR
DHSR and Young’s Modulus Crossplot
E
E
E: Young’s Modulus